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<!DOCTYPE html>
<html lang="en">
<!-- Copyrights @ https://github.com/azlanajju/lab -->
<!-- Author : Azlan -->
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="icon" type="image/x-icon" href="/error/lab.ico" />
<link rel="stylesheet" href="style.css" />
<!-- font awesom icons -->
<link
rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"
/>
<link
rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/prism-themes/1.9.0/prism-a11y-dark.min.css"
/>
<!-- google fonts -->
<link
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href="https://fonts.googleapis.com/css?family=Product+Sans:regular,bold,italic,thin,light,bolditalic,black,blackitalic,thinitalic,lightitalic,medium,mediumitalic"
/>
<title>Lab Programs</title>
</head>
<body>
<div class="head_portion1">
<div class="menu_icon" onclick="openNav()">
<i class="fas fa-ellipsis-v"></i>
</div>
Program.java - DAA - Visual Studio Code
</div>
<div class="head_portion2">
<a href="https://3rdsemlab.netlify.app/">Sem3</a>
<a href="https://4thsem-lab.netlify.app/">Sem4</a>
<a href="https://5th-semlab.netlify.app/">Sem5</a>
<a href="https://6thsem-lab.netlify.app/">Sem6</a>
<a href="" class="sem_active">Sem7</a>
<a href="">Sem8</a>
</div>
<div class="side_icons">
<div class="side_icon_img active_icon">
<img src="./icons/copy.png" alt="" />
</div>
<div class="side_icon_img">
<img src="./icons/search.png" alt="" />
</div>
<div class="side_icon_img">
<img src="./icons/gitcontrol.png" alt="" />
</div>
<div class="side_icon_img">
<img src="./icons/debug.png" alt="" />
</div>
<div class="side_icon_img">
<img src="./icons/extension.png" alt="" />
</div>
</div>
<div id="pgmList" class="pgm_number">
<div class="explorer">
<p>EXPLORER</p>
<p class="explorer_dots">...</p>
</div>
<div class="vs_code"><i class="fas fa-chevron-down"></i> VS_CODE</div>
<div class="subject_name">
<div class="sub_heading"><i class="fas fa-chevron-down"></i> AIML</div>
<p onclick="switchProgram('program_container1')">
<img src="./icons/python.png" alt="" /> program1.py
</p>
<p onclick="switchProgram('program_container2')">
<img src="./icons/python.png" alt="" /> program2.py
</p>
<p onclick="switchProgram('program_container3')">
<img src="./icons/python.png" alt="" /> program3.py
</p>
<p onclick="switchProgram('program_container4')">
<img src="./icons/python.png" alt="" /> program4.py
</p>
<p onclick="switchProgram('program_container5')">
<img src="./icons/python.png" alt="" /> program5.py
</p>
<p onclick="switchProgram('program_container6')">
<img src="./icons/python.png" alt="" /> program6.py
</p>
<p onclick="switchProgram('program_container7')">
<img src="./icons/python.png" alt="" /> program7.py
</p>
<p onclick="switchProgram('program_container8')">
<img src="./icons/python.png" alt="" /> program8.py
</p>
<p onclick="switchProgram('program_container9')">
<img src="./icons/python.png" alt="" /> program9.py
</p>
</div>
</div>
<!-- program section -->
<!-- program 1 -->
<div id="program_container1" class="textarea_container">
<div class="line_numbers">
<p>1</p>
<p>2</p>
<p>3</p>
<p>4</p>
<p>5</p>
<p>6</p>
<p>7</p>
<p>8</p>
<p>9</p>
<p>10</p>
<p>11</p>
<p>12</p>
<p>13</p>
<p>14</p>
<p>15</p>
<p>16</p>
<p>17</p>
<p>18</p>
<p>19</p>
<p>20</p>
<p>21</p>
<p>22</p>
</div>
<div class="copy_btn">
<div class="pgm_num">
Program 1
<p onclick="closeProgram()">X</p>
</div>
<div class="copy_icon" onclick="copyProgram('program1', 'copied1')">
<i class="far fa-copy"></i>
<div class="copied" id="copied1"></div>
</div>
</div>
<div class="text_area" id="program1">
<pre>
<code class="language-java">
def aStarAlgo(start_node, stop_node):
open_set = set(start_node)
closed_set = set()
g = {} #store distance from starting node
parents = {} # parents contains an adjacency map of all nodes
#distance of starting node from itself is zero
g[start_node] = 0
#start_node is root node i.e it has no parent nodes
#so start_node is set to its own parent node
parents[start_node] = start_node
while len(open_set) > 0:
n = None
#node with lowest f() is found
for v in open_set:
if n == None or g[v] + heuristic(v) < g[n] + heuristic(n):
n = v
if n == stop_node or Graph_nodes[n] == None:
pass
else:
for (m, weight) in get_neighbors(n):
#nodes 'm' not in first and last set are added to first
#n is set its parent
if m not in open_set and m not in closed_set:
open_set.add(m)
parents[m] = n
g[m] = g[n] + weight
#for each node m,compare its distance from start i.e g(m) to the
#from start through n node
else:
if g[m] > g[n] + weight:
#update g(m)
g[m] = g[n] + weight
#change parent of m to n
parents[m] = n
#if m in closed set,remove and add to open
if m in closed_set:
closed_set.remove(m)
open_set.add(m)
if n == None:
print('Path does not exist!')
return None
# if the current node is the stop_node
# then we begin reconstructin the path from it to the start_node
if n == stop_node:
path = []
while parents[n] != n:
path.append(n)
n = parents[n]
path.append(start_node)
path.reverse()
print('Path found: {}'.format(path))
return path
# remove n from the open_list, and add it to closed_list
# because all of his neighbors were inspected
open_set.remove(n)
closed_set.add(n)
print('Path does not exist!')
return None
#define fuction to return neighbor and its distance
#from the passed node
def get_neighbors(v):
if v in Graph_nodes:
return Graph_nodes[v]
else:
return None
#for simplicity we ll consider heuristic distances given
#and this function returns heuristic distance for all nodes
def heuristic(n):
H_dist = {
'A': 11,
'B': 6,
'C': 5,
'D': 7,
'E': 3,
'F': 6,
'G': 5,
'H': 3,
'I': 1,
'J': 0
}
return H_dist[n]
#Describe your graph here
Graph_nodes = {
'A': [('B', 6), ('F', 3)],
'B': [('A', 6), ('C', 3), ('D', 2)],
'C': [('B', 3), ('D', 1), ('E', 5)],
'D': [('B', 2), ('C', 1), ('E', 8)],
'E': [('C', 5), ('D', 8), ('I', 5), ('J', 5)],
'F': [('A', 3), ('G', 1), ('H', 7)],
'G': [('F', 1), ('I', 3)],
'H': [('F', 7), ('I', 2)],
'I': [('E', 5), ('G', 3), ('H', 2), ('J', 3)],
}
aStarAlgo('A', 'J')
</code>
</pre>
</div>
</div>
<!-- program 2 -->
<div
id="program_container2"
class="textarea_container"
style="display: none"
>
<div class="line_numbers">
<p>1</p>
<p>2</p>
<p>3</p>
<p>4</p>
<p>5</p>
<p>6</p>
<p>7</p>
<p>8</p>
<p>9</p>
<p>10</p>
<p>11</p>
<p>12</p>
<p>13</p>
<p>14</p>
<p>15</p>
<p>16</p>
<p>17</p>
<p>18</p>
<p>19</p>
<p>20</p>
<p>21</p>
<p>22</p>
</div>
<div class="copy_btn">
<div class="pgm_num">
Program 2
<p onclick="closeProgram()">X</p>
</div>
<div class="copy_icon" onclick="copyProgram('program2', 'copied2')">
<i class="far fa-copy"></i>
<div class="copied" id="copied2"></div>
</div>
</div>
<div class="text_area" id="program2">
<pre>
<code class="language-java">
class Graph:
def __init__(self, graph, heuristicNodeList, startNode): #instantiate graph object with graph topology, heuristic values, start node
self.graph = graph
self.H=heuristicNodeList
self.start=startNode
self.parent={}
self.status={}
self.solutionGraph={}
def applyAOStar(self): # starts a recursive AO* algorithm
self.aoStar(self.start, False)
def getNeighbors(self, v): # gets the Neighbors of a given node
return self.graph.get(v,'')
def getStatus(self,v): # return the status of a given node
return self.status.get(v,0)
def setStatus(self,v, val): # set the status of a given node
self.status[v]=val
def getHeuristicNodeValue(self, n):
return self.H.get(n,0) # always return the heuristic value of a given node
def setHeuristicNodeValue(self, n, value):
self.H[n]=value # set the revised heuristic value of a given node
def printSolution(self):
print("FOR GRAPH SOLUTION, TRAVERSE THE GRAPH FROM THE START NODE:",self.start)
print("------------------------------------------------------------")
print(self.solutionGraph)
print("------------------------------------------------------------")
def computeMinimumCostChildNodes(self, v): # Computes the Minimum Cost of child nodes of a given node v
minimumCost=0
costToChildNodeListDict={}
costToChildNodeListDict[minimumCost]=[]
flag=True
for nodeInfoTupleList in self.getNeighbors(v): # iterate over all the set of child node/s
cost=0
nodeList=[]
for c, weight in nodeInfoTupleList:
cost=cost+self.getHeuristicNodeValue(c)+weight
nodeList.append(c)
if flag==True: # initialize Minimum Cost with the cost of first set of child node/s
minimumCost=cost
costToChildNodeListDict[minimumCost]=nodeList # set the Minimum Cost child node/s
flag=False
else: # checking the Minimum Cost nodes with the current Minimum Cost
if minimumCost>cost:
minimumCost=cost
costToChildNodeListDict[minimumCost]=nodeList # set the Minimum Cost child node/s
return minimumCost, costToChildNodeListDict[minimumCost] # return Minimum Cost and Minimum Cost child node/s
def aoStar(self, v, backTracking): # AO* algorithm for a start node and backTracking status flag
print("HEURISTIC VALUES :", self.H)
print("SOLUTION GRAPH :", self.solutionGraph)
print("PROCESSING NODE :", v)
print("-----------------------------------------------------------------------------------------")
if self.getStatus(v) >= 0: # if status node v >= 0, compute Minimum Cost nodes of v
minimumCost, childNodeList = self.computeMinimumCostChildNodes(v)
print(minimumCost, childNodeList)
self.setHeuristicNodeValue(v, minimumCost)
self.setStatus(v,len(childNodeList))
solved=True # check the Minimum Cost nodes of v are solved
for childNode in childNodeList:
self.parent[childNode]=v
if self.getStatus(childNode)!=-1:
solved=solved & False
if solved==True: # if the Minimum Cost nodes of v are solved, set the current node status as solved(-1)
self.setStatus(v,-1)
self.solutionGraph[v]=childNodeList # update the solution graph with the solved nodes which may be a part of solution
if v!=self.start: # check the current node is the start node for backtracking the current node value
self.aoStar(self.parent[v], True) # backtracking the current node value with backtracking status set to true
if backTracking==False: # check the current call is not for backtracking
for childNode in childNodeList: # for each Minimum Cost child node
self.setStatus(childNode,0) # set the status of child node to 0(needs exploration)
self.aoStar(childNode, False) # Minimum Cost child node is further explored with backtracking status as false
#for simplicity we ll consider heuristic distances given
print ("Graph - 1")
h1 = {'A': 1, 'B': 6, 'C': 2, 'D': 12, 'E': 2, 'F': 1, 'G': 5, 'H': 7, 'I': 7, 'J': 1}
graph1 = {
'A': [[('B', 1), ('C', 1)], [('D', 1)]],
'B': [[('G', 1)], [('H', 1)]],
'C': [[('J', 1)]],
'D': [[('E', 1), ('F', 1)]],
'G': [[('I', 1)]]
}
G1= Graph(graph1, h1, 'A')
G1.applyAOStar()
G1.printSolution()
</code>
</pre>
</div>
</div>
<!-- program 3 -->
<div
id="program_container3"
class="textarea_container"
style="display: none"
>
<div class="line_numbers">
<p>1</p>
<p>2</p>
<p>3</p>
<p>4</p>
<p>5</p>
<p>6</p>
<p>7</p>
<p>8</p>
<p>9</p>
<p>10</p>
<p>11</p>
<p>12</p>
<p>13</p>
<p>14</p>
<p>15</p>
<p>16</p>
<p>17</p>
<p>18</p>
<p>19</p>
<p>20</p>
<p>21</p>
<p>22</p>
</div>
<div class="copy_btn">
<div class="pgm_num">
Program 3
<p onclick="closeProgram()">X</p>
</div>
<div class="copy_icon" onclick="copyProgram('program3', 'copied3')">
<i class="far fa-copy"></i>
<div class="copied" id="copied3"></div>
</div>
</div>
<div class="text_area" id="program3">
<pre>
<code class="language-java" >
import numpy as np
import pandas as pd
data = pd.read_csv('Book1.csv')
concepts = np.array(data.iloc[:,0:-1])
print("\nInstances are:\n",concepts)
target = np.array(data.iloc[:,-1])
print("\nTarget Values are: ",target)
def learn(concepts, target):
specific_h = concepts[0].copy()
print("\nInitialization of specific_h and genearal_h")
print("\nSpecific Boundary: ", specific_h)
general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]
print("\nGeneric Boundary: ",general_h)
for i, h in enumerate(concepts):
print("\nInstance", i+1 , "is ", h)
if target[i] == "yes":
print("Instance is Positive ")
for x in range(len(specific_h)):
if h[x]!= specific_h[x]:
specific_h[x] ='?'
general_h[x][x] ='?'
if target[i] == "no":
print("Instance is Negative ")
for x in range(len(specific_h)):
if h[x]!= specific_h[x]:
general_h[x][x] = specific_h[x]
else:
general_h[x][x] = '?'
print("Specific Bundary after ", i+1, "Instance is ", specific_h)
print("Generic Boundary after ", i+1, "Instance is ", general_h)
print("\n")
indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]
for i in indices:
general_h.remove(['?', '?import numpy as np
import pandas as pd
data = pd.read_csv('Book1.csv')
concepts = np.array(data.iloc[:,0:-1])
print("\nInstances are:\n",concepts)
target = np.array(data.iloc[:,-1])
print("\nTarget Values are: ",target)
def learn(concepts, target):
specific_h = concepts[0].copy()
print("\nInitialization of specific_h and genearal_h")
print("\nSpecific Boundary: ", specific_h)
general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]
print("\nGeneric Boundary: ",general_h)
for i, h in enumerate(concepts):
print("\nInstance", i+1 , "is ", h)
if target[i] == "yes":
print("Instance is Positive ")
for x in range(len(specific_h)):
if h[x]!= specific_h[x]:
specific_h[x] ='?'
general_h[x][x] ='?'
if target[i] == "no":
print("Instance is Negative ")
for x in range(len(specific_h)):
if h[x]!= specific_h[x]:
general_h[x][x] = specific_h[x]
else:
general_h[x][x] = '?'
print("Specific Bundary after ", i+1, "Instance is ", specific_h)
print("Generic Boundary after ", i+1, "Instance is ", general_h)
print("\n")
indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]
for i in indices:
general_h.remove(['?', '?', '?', '?', '?', '?'])
return specific_h, general_h
s_final, g_final = learn(concepts, target)
print("Final Specific_h: ", s_final, sep="\n")
print("Final General_h: ", g_final, sep="\n")
</code>
</pre>
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<pre>
<code class="language-java">
import pandas as pd
import math
import numpy as np
data = pd.read_csv("dataset.csv")
features = [feat for feat in data]
features.remove("answer")
class Node:
def __init__(self):
self.children = []
self.value = ""
self.isLeaf = False
self.pred = ""
def entropy(examples):
pos = 0.0
neg = 0.0
for _, row in examples.iterrows():
if row["answer"] == "yes":
pos += 1
else:
neg += 1
if pos == 0.0 or neg == 0.0:
return 0.0
else:
p = pos / (pos + neg)
n = neg / (pos + neg)
return -(p * math.log(p, 2) + n * math.log(n, 2))
def info_gain(examples, attr):
uniq = np.unique(examples[attr])
#print ("\n",uniq)
gain = entropy(examples)
#print ("\n",gain)
for u in uniq:
subdata = examples[examples[attr] == u]
#print ("\n",subdata)
sub_e = entropy(subdata)
gain -= (float(len(subdata)) / float(len(examples))) * sub_e
#print ("\n",gain)
return gain
def ID3(examples, attrs):
root = Node()
max_gain = 0
max_feat = ""
for feature in attrs:
#print ("\n",examples)
gain = info_gain(examples, feature)
if gain > max_gain:
max_gain = gain
max_feat = feature
root.value = max_feat
#print ("\nMax feature attr",max_feat)
uniq = np.unique(examples[max_feat])
#print ("\n",uniq)
for u in uniq:
#print ("\n",u)
subdata = examples[examples[max_feat] == u]
#print ("\n",subdata)
if entropy(subdata) == 0.0:
newNode = Node()
newNode.isLeaf = True
newNode.value = u
newNode.pred = np.unique(subdata["answer"])
root.children.append(newNode)
else:
dummyNode = Node()
dummyNode.value = u
new_attrs = attrs.copy()
new_attrs.remove(max_feat)
child = ID3(subdata, new_attrs)
dummyNode.children.append(child)
root.children.append(dummyNode)
return root
def printTree(root: Node, depth=0):
for i in range(depth):
print("\t", end="")
print(root.value, end="")
if root.isLeaf:
print(" -> ", root.pred)
print()
for child in root.children:
printTree(child, depth + 1)
def classify(root: Node, new):
for child in root.children:
if child.value == new[root.value]:
if child.isLeaf:
print ("Predicted Label for new example", new," is:", child.pred)
exit
else:
classify (child.children[0], new)
root = ID3(data, features)
print("Decision Tree is:")
printTree(root)
print ("------------------")
new = {"outlook":"sunny", "temperature":"hot", "humidity":"normal", "wind":"strong"}
classify (root, new)
</code>
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<pre>
<code class="language-java ">
import numpy as np
X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float)
y = np.array(([92], [86], [89]), dtype=float)
X = X/np.amax(X,axis=0) #maximum of X array longitudinally
y = y/100
#Sigmoid Function
def sigmoid (x):
return 1/(1 + np.exp(-x))
#Derivative of Sigmoid Function
def derivatives_sigmoid(x):
return x * (1 - x)
#Variable initialization
epoch=5 #Setting training iterations
lr=0.1 #Setting learning rate
inputlayer_neurons = 2 #number of features in data set
hiddenlayer_neurons = 3 #number of hidden layers neurons
output_neurons = 1 #number of neurons at output layer
#weight and bias initialization
wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))
bh=np.random.uniform(size=(1,hiddenlayer_neurons))
wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons))
bout=np.random.uniform(size=(1,output_neurons))
#draws a random range of numbers uniformly of dim x*y
for i in range(epoch):
#Forward Propogation
hinp1=np.dot(X,wh)
hinp=hinp1 + bh
hlayer_act = sigmoid(hinp)
outinp1=np.dot(hlayer_act,wout)
outinp= outinp1+bout
output = sigmoid(outinp)
#Backpropagation
EO = y-output
outgrad = derivatives_sigmoid(output)
d_output = EO * outgrad
EH = d_output.dot(wout.T)
hiddengrad = derivatives_sigmoid(hlayer_act)#how much hidden layer wts contributed to error
d_hiddenlayer = EH * hiddengrad
wout += hlayer_act.T.dot(d_output) *lr # dotproduct of nextlayererror and currentlayerop
wh += X.T.dot(d_hiddenlayer) *lr
print ("-----------Epoch-", i+1, "Starts----------")
print("Input: \n" + str(X))
print("Actual Output: \n" + str(y))
print("Predicted Output: \n" ,output)
print ("-----------Epoch-", i+1, "Ends----------\n")
print("Input: \n" + str(X))
print("Actual Output: \n" + str(y))
print("Predicted Output: \n" ,output)
</code>
</pre>
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<pre>
<code class="language-java">
import csv
import random
import math
def loadcsv(filename):
lines = csv.reader(open(filename, "r"));
dataset = list(lines)
for i in range(len(dataset)):
#converting strings into numbers for processing
dataset[i] = [float(x) for x in dataset[i]]
return dataset
def splitdataset(dataset, splitratio):
#67% training size
trainsize = int(len(dataset) * splitratio);
trainset = []
copy = list(dataset);
while len(trainset) < trainsize:
#generate indices for the dataset list randomly to pick ele for training data
index = random.randrange(len(copy));
trainset.append(copy.pop(index))
return [trainset, copy]
def separatebyclass(dataset):
separated = {} #dictionary of classes 1 and 0
#creates a dictionary of classes 1 and 0 where the values are
#the instances belonging to each class
for i in range(len(dataset)):
vector = dataset[i]
if (vector[-1] not in separated):
separated[vector[-1]] = []
separated[vector[-1]].append(vector)
return separated
def mean(numbers):
return sum(numbers)/float(len(numbers))
def stdev(numbers):
avg = mean(numbers)
variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1)
return math.sqrt(variance)
def summarize(dataset): #creates a dictionary of classes
summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)];
del summaries[-1] #excluding labels +ve or -ve
return summaries
def summarizebyclass(dataset):
separated = separatebyclass(dataset);
#print(separated)
summaries = {}
for classvalue, instances in separated.items():
#for key,value in dic.items()
#summaries is a dic of tuples(mean,std) for each class value
summaries[classvalue] = summarize(instances) #summarize is used to cal to mean and std
return summaries
def calculateprobability(x, mean, stdev):
exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))
return (1 / (math.sqrt(2*math.pi) * stdev)) * exponent
def calculateclassprobabilities(summaries, inputvector):
probabilities = {} # probabilities contains the all prob of all class of test data
for classvalue, classsummaries in summaries.items():#class and attribute information as mean and sd
probabilities[classvalue] = 1
for i in range(len(classsummaries)):
mean, stdev = classsummaries[i] #take mean and sd of every attribute for class 0 and 1 seperaely
x = inputvector[i] #testvector's first attribute
probabilities[classvalue] *= calculateprobability(x, mean, stdev);#use normal dist
return probabilities
def predict(summaries, inputvector): #training and test data is passed
probabilities = calculateclassprobabilities(summaries, inputvector)
bestLabel, bestProb = None, -1
for classvalue, probability in probabilities.items():#assigns that class which has he highest prob
if bestLabel is None or probability > bestProb:
bestProb = probability
bestLabel = classvalue
return bestLabel
def getpredictions(summaries, testset):
predictions = []
for i in range(len(testset)):
result = predict(summaries, testset[i])
predictions.append(result)
return predictions
def getaccuracy(testset, predictions):
correct = 0
for i in range(len(testset)):
if testset[i][-1] == predictions[i]:
correct += 1
return (correct/float(len(testset))) * 100.0
def main():
filename = 'naivedata.csv'
splitratio = 0.67
dataset = loadcsv(filename);
trainingset, testset = splitdataset(dataset, splitratio)
print('Split {0} rows into train={1} and test={2} rows'.format(len(dataset), len(trainingset), len(testset)))
# prepare model
summaries = summarizebyclass(trainingset);
#print(summaries)
# test model
predictions = getpredictions(summaries, testset) #find the predictions of test data with the training data
accuracy = getaccuracy(testset, predictions)
print('Accuracy of the classifier is : {0}%'.format(accuracy))
main()
</code>
</pre>
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